Symbolic Discovery of Optimization Algorithms 中所提出优化器Lion的代码

Lion是谷歌最近提出的一个优化器，声称对大批量或大模型的训练效果挺好的，官方提供有各个版本的代码（包括pytorch版本的）。

论文下载地址：https://arxiv.org/abs/2302.06675

代码地址：https://github.com/google/automl/tree/master/lion

由于我搞深度学习主要使用Pytorch，所以这里只记录pytorch版本的代码。使用起来也挺方便的，和AdamW这些优化器一样的用法（至少我用起来没啥毛病）。

"""PyTorch implementation of the Lion optimizer."""
import torch
from torch.optim.optimizer import Optimizer

class Lion(Optimizer):
  r"""Implements Lion algorithm."""

  def __init__(self, params, lr=1e-4, betas=(0.9, 0.99), weight_decay=0.0):
    """Initialize the hyperparameters.
    Args:
      params (iterable): iterable of parameters to optimize or dicts defining
        parameter groups
      lr (float, optional): learning rate (default: 1e-4)
      betas (Tuple[float, float], optional): coefficients used for computing
        running averages of gradient and its square (default: (0.9, 0.99))
      weight_decay (float, optional): weight decay coefficient (default: 0)
    """

    if not 0.0 <= lr:
      raise ValueError('Invalid learning rate: {}'.format(lr))
    if not 0.0 <= betas[0] < 1.0:
      raise ValueError('Invalid beta parameter at index 0: {}'.format(betas[0]))
    if not 0.0 <= betas[1] < 1.0:
      raise ValueError('Invalid beta parameter at index 1: {}'.format(betas[1]))
    defaults = dict(lr=lr, betas=betas, weight_decay=weight_decay)
    super().__init__(params, defaults)

  @torch.no_grad()
  def step(self, closure=None):
    """Performs a single optimization step.
    Args:
      closure (callable, optional): A closure that reevaluates the model
        and returns the loss.
    Returns:
      the loss.
    """
    loss = None
    if closure is not None:
      with torch.enable_grad():
        loss = closure()

    for group in self.param_groups:
      for p in group['params']:
        if p.grad is None:
          continue

        # Perform stepweight decay
        p.data.mul_(1 - group['lr'] * group['weight_decay'])

        grad = p.grad
        state = self.state[p]
        # State initialization
        if len(state) == 0:
          # Exponential moving average of gradient values
          state['exp_avg'] = torch.zeros_like(p)

        exp_avg = state['exp_avg']
        beta1, beta2 = group['betas']

        # Weight update
        update = exp_avg * beta1 + grad * (1 - beta1)
        p.add_(torch.sign(update), alpha=-group['lr'])
        # Decay the momentum running average coefficient
        exp_avg.mul_(beta2).add_(grad, alpha=1 - beta2)

    return loss